code: def test_explainer_method_preprocessing(
      linear_cls_model, random_cls_dataset
  ):
      """
      Preprocessing method returns correct data format for correct inputs.
      """
      backend, model, loss_fn, target_fn = linear_cls_model
      (X_train, Y_train), (_, _) = random_cls_dataset
  
      explainer = GradientSimilarity(
          predictor=model,
          loss_fn=loss_fn,
          backend=backend,
      )
  
      # test stored gradients
      explainer.fit(X_train=X_train, Y_train=Y_train)
      X, Y = explainer._preprocess_args(X_train[0:3])
      assert X.shape == (3, 10)
      assert Y.shape == (3,)
  
      X, Y = explainer._preprocess_args(X_train[0])
      assert X.shape == (1, 10)
  
      X, Y = explainer._preprocess_args(X_train[0])
      assert X.shape == (1, 10)
  
      grad_X_test = explainer.backend.get_grads(model, X, Y, loss_fn)
      assert grad_X_test.shape == (110,)
  
file_path: alibi/alibi/explainers/tests/test_simiarlity/test_grad_methods_unit.py
imports: import pytest
  from alibi.explainers.tests.test_simiarlity.conftest import get_flattened_model_parameters
  from alibi.explainers.similarity.grad import GradientSimilarity, Task
  from alibi.utils import _get_options_string
label_desc: Use image node_id 2 for calling a global function with example usage: test_explainer_method_preprocessing(linear_cls_model, random_cls_dataset) without return types